Blind stereoscopic image quality assessment by deep neural network of multi-level feature fusion

In this paper, we propose an effective blind image quality assessment (BIQA) method for stereoscopic images by deep neural network (DNN) of multi-level feature fusion (MLFF) inspired by the multi-scale characteristics and binocular properties of the human visual system (HVS). Specifically, we firstly feed the left- and right-view images into a weight sharing convolutional neural network (CNN) for jointly feature extraction. To aggregate multi-level features, we concatenate the low-, middle-, and high-level feature maps of stereoscopic images to simulate the complicated visual interaction processing in the HVS. Two fully connected layers are used to build the nonlinear mapping from the highly abstract features to the quality scores of stereoscopic images. The experiments conducted on two public databases prove the validity of the proposed MLFF method.